基于BioBERT的文本挖掘,用于将先前的知识纳入遗传网络模型的推断中
Jaskaran Kaur Gill1, Madhu Chetty1, Suryani Lim1
1Health Innovation and Transformation Centre, Federation University, Victoria, 3842, Australia.
Computers in biology and medicine
|January 3, 2025
概括
这项研究介绍了PRESS,这是一种新的方法,它结合了先前的生物学知识,以更准确,更有效地重建基因调节网络 (GRNs). PRESS提高了复杂生物系统的计算速度和预测准确度.
科学领域:
- 系统生物学 系统生物学
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
背景情况:
- 基因调控网络 (GRN) 的重建对于理解细胞过程和疾病机制至关重要.
- 使用非线性微分方程的S系统模型捕捉了复杂的生物动态,但随着网络规模的增加而面临计算挑战.
- 先前的生物知识整合可以减轻计算需求,提高GRN推断准确度.
研究的目的:
- 引入PRESS (先验知识增强S系统模型),用于准确的GRN重建的自动化框架.
- 通过结合从科学文献中提取的先前知识来提高GRN推断的速度和准确性.
- 在大规模网络分析中解决传统S系统模型的计算局限性.
主要方法:
- 开发了PRESS,这是一个集成模型,将自动化先前知识提取与S系统GRN重建方法相结合.
- 利用基于BioBERT的基因相互作用提取框架,用于针对性的遗传关系提取和调控基因预测.
- 在优化算法中实施了一种新的健身评估,以限制调节基因,模仿真正的GRNs.
主要成果:
- 证明了GRN重建的计算成本大幅降低.
- 与现有方法相比,在预测准确度方面取得了显著的改进.
- 使用大肠杆菌子网络和基准SOS数据集验证了PRESS的有效性.
结论:
- 通过整合先前的生物知识,PRESS提供了一种新且有效的自动化GRN重建方法.
- 该方法显著提高了计算效率和预测准确性,克服了传统S系统模型的局限性.
- PRESS代表了计算生物学在分析复杂的基因调节系统方面取得的重大进展.
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